Client

The client is a leading American multinational energy corporation with a global presence across the oil and gas value chain. Vertically integrated, it operates across upstream exploration, midstream transportation, and downstream refining, marketing, and power generation. The company also has a strong foothold in chemicals manufacturing and energy solutions.

As part of its strategic push toward operational excellence and sustainability, the client sought to modernize its oilfield operations through intelligent data platforms and real-time asset visibility. The goal was to leverage predictive maintenance and digital twin technologies to minimize equipment downtime, enhance reliability, and drive data-informed decision-making across their production and surface operations.

Need for Change

Oil and gas operations are becoming increasingly complex, with expanding asset networks, legacy systems, and disparate data sources creating significant challenges in maintaining operational efficiency. Surface operations, in particular, generate massive volumes of equipment, maintenance, and production data, often trapped in silos and inconsistent formats. This fragmentation limited collaboration, slowed analytics, and raised compliance risks across critical workflows.

To overcome these constraints, the client aimed to establish a unified Surface Data Platform that could centralize operational datasets, enable predictive maintenance, and support Digital Twin models. The initiative aimed to standardize data, improve reliability, and empower field teams with real-time, actionable insights. This laid a strong foundation for oil and gas data modernization.

Business Challenges

Despite a clear vision for modernization, the client’s data environment remained fragmented and operationally rigid. The absence of a centralized Surface Data Hub meant that vital operational data, ranging from equipment and tag information to work orders, was scattered across disconnected systems. This fragmentation weakened collaboration, slowed decision-making, and limited the effectiveness of analytics-driven initiatives. Key pain points included:

Disparate Systems

Critical equipment, tag, and work order data were locked within different systems, making it hard to get a single, accurate view of operations.

Poor Data Quality

Inconsistent naming conventions, missing attributes, and duplicating records undermined data confidence.

High Integration Costs

Teams spent significant time and resources manually connecting systems and reconciling data, increasing project costs and slowing delivery.

Security and Compliance Risks

With no centralized governance, enforcing data access policies or tracking audit trails became complex and risky.

Limited Scalability

Existing infrastructure struggled to support real-time analytics or scale predictive models, restricting growth and agility.

Governance and Empowerment Deficiency

The absence of structured governance kept data teams reactive rather than empowered, reducing innovation potential.

LTM Solution

To overcome data fragmentation and operational silos, LTM built a scalable, cloud-native Surface Data Hub. It is a unified platform, enabling seamless access to equipment, tag, and work order data across business units. This foundation powered real-time analytics, predictive maintenance, and Digital Twin initiatives, helping the client achieve efficiency, reliability, and innovation across the energy value chain. Key highlights of the solution included:

Tech Stack

• Backend Technologies 

            Azure SQL Server, Spark SQL, Share point

• Cloud Resources

           Azure Databricks, Azure Data Lake, Azure Storage account, Azure Data  Factory

• DevOps

        CI/CD pipelines, Azure DevOps

Business Benefits

The initiative accelerated the client’s oil and gas data modernization journey, transforming fragmented systems into a connected, intelligent data ecosystem. Through 30+ scalable data products and 40+ automated validation checks, the client operated with greater efficiency, transparency, and trust in its data. By enabling Digital Twin in oil and gas, the platform enhanced real-time visibility and predictive insights across operations.

Increased Operational Efficiency

Automated workflows reduced processing time from 9 to 3 hours per cycle, freeing field and maintenance teams to focus on proactive operations and performance improvement.

Enabled Cross-BU Collaboration

Standardized frameworks linked equipment, tag, and work order data, empowering engineering teams across refineries to collaborate effectively on predictive maintenance and asset optimization.

Strengthened Governance and Compliance

Built-in auditing and traceability reinforced compliance, providing visibility into data lineage and transformation across business units.

Reduced Time and Costs

Validation and data quality checks that once took up to two days were completed in under four hours. This reduced operational delays and also lowered data management costs.

Ensured Enterprise-Wide Consistency

Unified data standards eliminated duplication and inconsistencies, ensuring that all teams operated from a single, trusted source of truth.

Maximized Automation and Self-Service

Automated data pipelines and curated datasets enabled business users to access accurate, real-time information without IT dependence, speeding up insight generation.

Maintained Data Security and Compliance

Multi-layered security safeguards protected sensitive operational data, maintaining trust and compliance.

Delivered Federated, On-Demand Access

Secure, governed access allowed teams and AI systems to locate and consume enterprise data on demand, supporting agile decision-making and a connected digital ecosystem.

Conclusion

By modernizing operational data through the Surface Data Hub, the client built a trusted foundation for predictive intelligence and agility. The initiative strengthened collaboration, ensured compliance, and advanced oil and gas data modernization efforts.

Building on this success, the next phase extends the Surface Data Hub to include maintenance inspection data products, deepening insights into asset health, inspection schedules, and compliance tracking. With enhanced monitoring, governance, and scalable frameworks for rapid onboarding of new data products, the client is well-positioned to scale predictive maintenance and accelerate Digital Twin adoption enterprise wide.